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	<id>https://wiki.socr.umich.edu/index.php?action=history&amp;feed=atom&amp;title=Two_Way_ANOVA</id>
	<title>Two Way ANOVA - Revision history</title>
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	<updated>2026-06-04T15:59:57Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>https://wiki.socr.umich.edu/index.php?title=Two_Way_ANOVA&amp;diff=2540&amp;oldid=prev</id>
		<title>IvoDinov at 05:48, 19 January 2007</title>
		<link rel="alternate" type="text/html" href="https://wiki.socr.umich.edu/index.php?title=Two_Way_ANOVA&amp;diff=2540&amp;oldid=prev"/>
		<updated>2007-01-19T05:48:37Z</updated>

		<summary type="html">&lt;p&gt;&lt;/p&gt;
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				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #222; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #222; text-align: center;&quot;&gt;Revision as of 05:48, 19 January 2007&lt;/td&gt;
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		<author><name>IvoDinov</name></author>
		
	</entry>
	<entry>
		<id>https://wiki.socr.umich.edu/index.php?title=Two_Way_ANOVA&amp;diff=1832&amp;oldid=prev</id>
		<title>Annie at 21:08, 31 July 2006</title>
		<link rel="alternate" type="text/html" href="https://wiki.socr.umich.edu/index.php?title=Two_Way_ANOVA&amp;diff=1832&amp;oldid=prev"/>
		<updated>2006-07-31T21:08:18Z</updated>

		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&amp;lt;pre&amp;gt;&lt;br /&gt;
/*&lt;br /&gt;
&lt;br /&gt;
July 2006. Annie Che &amp;lt;chea@stat.ucla.edu&amp;gt;. UCLA Statistics.&lt;br /&gt;
&lt;br /&gt;
Source of example data: An Introduction to Computational Statitics by Robert I Jennrich,&lt;br /&gt;
Page 207, example of regression on time for coins to reach bottom of fountains.&lt;br /&gt;
&lt;br /&gt;
*/&lt;br /&gt;
package edu.ucla.stat.SOCR.analyses.example;&lt;br /&gt;
&lt;br /&gt;
import java.util.HashMap;&lt;br /&gt;
import edu.ucla.stat.SOCR.analyses.data.Data;&lt;br /&gt;
import edu.ucla.stat.SOCR.analyses.data.DataType;&lt;br /&gt;
import edu.ucla.stat.SOCR.analyses.result.AnovaTwoWayResult;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
public class AnovaTwoWayExample {&lt;br /&gt;
	public static void main(String args[]) {&lt;br /&gt;
		String[] group1 = {&amp;quot;1&amp;quot;,&amp;quot;1&amp;quot;,&amp;quot;1&amp;quot;,&amp;quot;2&amp;quot;,&amp;quot;2&amp;quot;,&amp;quot;2&amp;quot;};&lt;br /&gt;
		String[] group2 = {&amp;quot;1&amp;quot;,&amp;quot;2&amp;quot;,&amp;quot;3&amp;quot;,&amp;quot;1&amp;quot;,&amp;quot;2&amp;quot;,&amp;quot;3&amp;quot;};&lt;br /&gt;
		double[] score = {93,136,198,88,148,279};&lt;br /&gt;
&lt;br /&gt;
		// you'll need to instantiate a data instance first.&lt;br /&gt;
		Data data = new Data();&lt;br /&gt;
&lt;br /&gt;
		/*********************************************************************&lt;br /&gt;
		then put the data into the Data Object.&lt;br /&gt;
		append the predictor data using method &amp;quot;addPredictor&amp;quot;.&lt;br /&gt;
		append the response data using method &amp;quot;addResponse&amp;quot;.&lt;br /&gt;
		**********************************************************************/&lt;br /&gt;
		data.addPredictor(&amp;quot;I&amp;quot;, group1, DataType.FACTOR);&lt;br /&gt;
		data.addPredictor(&amp;quot;J&amp;quot;, group2, DataType.FACTOR);&lt;br /&gt;
		data.addResponse(&amp;quot;Y&amp;quot;, score, DataType.QUANTITATIVE);&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
		try {&lt;br /&gt;
			AnovaTwoWayResult result = data.modelAnovaTwoWay();&lt;br /&gt;
			System.out.println(&amp;quot;result = &amp;quot; + result);&lt;br /&gt;
			if (result != null) {&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
				// Getting the model's parameter estiamtes and statistics.&lt;br /&gt;
				int dfCTotal = result.getDFTotal();&lt;br /&gt;
				int dfModel = result.getDFModel();&lt;br /&gt;
				int dfError = result.getDFError();&lt;br /&gt;
				System.out.println(&amp;quot;dfCTotal = &amp;quot; + dfCTotal);&lt;br /&gt;
				System.out.println(&amp;quot;dfModel = &amp;quot; + dfModel);&lt;br /&gt;
				System.out.println(&amp;quot;dfError = &amp;quot; + dfError);&lt;br /&gt;
&lt;br /&gt;
				double rssTotal = result.getRSSTotal();&lt;br /&gt;
				double rssModel = result.getRSSModel();&lt;br /&gt;
				double rssError = result.getRSSError();&lt;br /&gt;
				System.out.println(&amp;quot;rssTotal = &amp;quot; + rssTotal);&lt;br /&gt;
				System.out.println(&amp;quot;rssModel = &amp;quot; + rssModel);&lt;br /&gt;
				System.out.println(&amp;quot;rssError = &amp;quot; + rssError);&lt;br /&gt;
&lt;br /&gt;
				double mssModel = result.getMSSModel();&lt;br /&gt;
				double mssError = result.getMSSError();&lt;br /&gt;
&lt;br /&gt;
				System.out.println(&amp;quot;mssModel = &amp;quot; + mssModel);&lt;br /&gt;
				System.out.println(&amp;quot;mssError = &amp;quot; + mssError);&lt;br /&gt;
&lt;br /&gt;
				double fValue = result.getFValue();&lt;br /&gt;
				String pValue = result.getPValue();&lt;br /&gt;
				System.out.println(&amp;quot;fValue = &amp;quot; + fValue);&lt;br /&gt;
				System.out.println(&amp;quot;pValue = &amp;quot; + pValue);&lt;br /&gt;
&lt;br /&gt;
				String[] varList = result.getVariableList();&lt;br /&gt;
				int[] dfGroup = result.getDFGroup();&lt;br /&gt;
				double[] rssGourp = result.getRSSGroup();&lt;br /&gt;
				double[] mseGourp = result.getMSEGroup();&lt;br /&gt;
				double[] fValueGroup = result.getFValueGroup();&lt;br /&gt;
				String[] pValueGroup = result.getPValueGroup();&lt;br /&gt;
				double[] residuals = result.getResiduals();&lt;br /&gt;
				double[] predicted = result.getPredicted();&lt;br /&gt;
&lt;br /&gt;
				// residuals after being sorted ascendantly.&lt;br /&gt;
				double[] sortedResiduals = result.getSortedResiduals();&lt;br /&gt;
&lt;br /&gt;
				// sortedResiduals after being standardized.&lt;br /&gt;
				double[] sortedStandardizedResiduals = &lt;br /&gt;
                                result.getSortedStandardizedResiduals();&lt;br /&gt;
&lt;br /&gt;
				// the original index of sortedResiduals, stored as integer array.&lt;br /&gt;
				int[] sortedResidualsIndex = result.getSortedResidualsIndex();&lt;br /&gt;
&lt;br /&gt;
				// the normal quantiles of sortedResiduals.&lt;br /&gt;
				double[] sortedNormalQuantiles = result.getSortedNormalQuantiles();&lt;br /&gt;
&lt;br /&gt;
				// sortedNormalQuantiles after being standardized.&lt;br /&gt;
				double[] sortedStandardizedNormalQuantiles = &lt;br /&gt;
                                result.getSortedStandardizedNormalQuantiles();&lt;br /&gt;
&lt;br /&gt;
				System.out.println(&amp;quot;dfCTotal = &amp;quot; + dfCTotal);&lt;br /&gt;
				System.out.println(&amp;quot;dfModel = &amp;quot; + dfModel);&lt;br /&gt;
				System.out.println(&amp;quot;dfError = &amp;quot; + dfError);&lt;br /&gt;
&lt;br /&gt;
				System.out.println(&amp;quot;rssTotal = &amp;quot; + rssTotal);&lt;br /&gt;
				System.out.println(&amp;quot;rssModel = &amp;quot; + rssModel);&lt;br /&gt;
				System.out.println(&amp;quot;rssError = &amp;quot; + rssError);&lt;br /&gt;
&lt;br /&gt;
				System.out.println(&amp;quot;mssModel = &amp;quot; + mssModel);&lt;br /&gt;
				System.out.println(&amp;quot;mssError = &amp;quot; + mssError);&lt;br /&gt;
&lt;br /&gt;
				System.out.println(&amp;quot;fValue = &amp;quot; + fValue);&lt;br /&gt;
				System.out.println(&amp;quot;pValue = &amp;quot; + pValue);&lt;br /&gt;
&lt;br /&gt;
				for (int i = 0; i &amp;lt; varList.length; i++) {&lt;br /&gt;
					System.out.println(&amp;quot;varList[&amp;quot;+i+&amp;quot;] = &amp;quot; + varList[i]);&lt;br /&gt;
				}&lt;br /&gt;
				for (int i = 0; i &amp;lt; residuals.length; i++) {&lt;br /&gt;
					System.out.println(&amp;quot;residuals[&amp;quot;+i+&amp;quot;] = &amp;quot; + residuals[i]);&lt;br /&gt;
				}&lt;br /&gt;
&lt;br /&gt;
			}&lt;br /&gt;
		} catch (Exception e) {&lt;br /&gt;
			System.out.println(e);&lt;br /&gt;
		}&lt;br /&gt;
	}&lt;br /&gt;
}&lt;br /&gt;
&amp;lt;/pre&amp;gt;&lt;/div&gt;</summary>
		<author><name>Annie</name></author>
		
	</entry>
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